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Use of deep learning for Characterization of Microfluidic Soft Sensors

IEEE robotics AND automation LETTERS 2018年第2期3卷 873-880页

作者： Han, Seunghyun Kim, Taekyoung Kim, Dooyoung Park, Yong-Lae Jo, Sungho Korea Adv Inst Sci & Technol Sch Comp Daejeon 305701 South Korea Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Inst Adv Machines & Design Seoul 08826 South Korea

Soft sensors made of highly deformable materials are one of the enabling technologies to various soft robotic systems, such as soft mobile robots, soft wearable robots, and soft grippers. However, major drawbacks of soft sensors compared with traditional sensors are their nonlinearity and hysteresis in response, which are common especially in microfluidic soft sensors. In this research, we propose to address the above issues of soft sensors by taking advantage of deep learning. We implemented a hierarchical recurrent sensing network, a type of recurrent neural network model, to the calibration of soft sensors for estimating the magnitude and the location of a contact pressure simultaneously. The proposed approach in this letter is not only able to model the nonlinear characteristic with hysteresis of the pressure response, but also find the location of the pressure.

关键词： Soft material robotics deep learning in robotics and automation force and tactile sensing

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SOZIL: Self-Optimal Zero-Shot Imitation learning

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2023年第4期15卷 2077-2088页

作者： Hao, Peng Lu, Tao Cui, Shaowei Wei, Junhang Cai, Yinghao Wang, Shuo Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China

Zero-shot imitation learning has demonstrated its superiority to learn complex robotic tasks with less human participation. Recent studies show convincing performance under the condition that the robot follows the demonstration strictly by the learned inverse model. However, these methods are difficult to achieve satisfactory performance in imitation when the demonstration is suboptimal, and the learning of the learned inverse models is vulnerable to label ambiguity issues. In this article, we propose self-optimal zero-shot imitation learning (SOZIL) to tackle these problems. The contribution of SOZIL is twofold. First, goal consistency loss (GCL) is designed to learn the multistep goal-conditioned policy from exploration data. By directly using the goal state as supervision, GCL solves the label ambiguity problem caused by trajectory and action diversity. Second, estimation-based keyframe extraction (EKE) is developed to optimize demonstrations. We formulate the keyframe extraction process as a path optimization problem under suboptimal control. By predicting the performance of the learned policy in executing transitions of any two states, EKE creates a directed graph containing all candidate paths and extracts keyframes by solving the graph's shortest path problem. Furthermore, the proposed method is evaluated with various simulated and real-world robotic manipulating experiments, such as cable harness assembly, rope manipulation, and block moving. Experimental results show that SOZIL achieves a superior success rate and manipulation efficiency than baselines.

关键词： deep learning in robotics and automation imitation learning keyframe demonstration learning from observation

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Interactive Gibson Benchmark: A Benchmark for Interactive Navigation in Cluttered Environments

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IEEE robotics AND automation LETTERS 2020年第2期5卷 713-720页

作者： Xia, Fei Shen, William B. Li, Chengshu Kasimbeg, Priya Tchapmi, Micael Edmond Toshev, Alexander Martin-Martin, Roberto Savarese, Silvio Stanford Univ SAIL Stanford CA 94305 USA Google Robot Mountain View CA 94043 USA

We present Interactive Gibson Benchmark, the first comprehensive benchmark for training and evaluating Interactive Navigation solutions. Interactive Navigation tasks are robot navigation problems where physical interaction with objects (e.g., pushing) is allowed and even encouraged to reach the goal. Our benchmark comprises two novel elements: 1) a new experimental simulated environment, the Interactive Gibson Environment, that generate photo-realistic images of indoor scenes and simulates realistic physical interactions of robots and common objects found in these scenes;2) the Interactive Navigation Score, a novel metric to study the interplay between navigation and physical interaction of Interactive Navigation solutions. We present and evaluate multiple learning-based baselines in Interactive Gibson Benchmark, and provide insights into regimes of navigation with different trade-offs between navigation, path efficiency and disturbance of surrounding objects. We make our benchmark publicly available(1) and encourage researchers from related robotics disciplines (e.g., planning, learning, control) to propose, evaluate, and compare their Interactive Navigation solutions in Interactive Gibson Benchmark.

关键词： Visual-based navigation deep learning in robotics and automation mobile manipulation

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Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs

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IEEE robotics AND automation LETTERS 2019年第2期4卷 2140-2147页

作者： Magana, Octavio Antonio Villarreal Barasuol, Victor Camurri, Marco Franceschi, Luca Focchi, Michele Pontil, Massimiliano Caldwell, Darwin G. Semini, Claudio Ist Italiano Tecnol Dynam Legged Syst Lab I-16163 Genoa Italy Univ Oxford Oxford Robot Inst Oxford OX2 6NN England Ist Italian Tecnol Computat Stat & Machine Learning I-16163 Genoa Italy Ist Italiano Tecnol Dept Adv Robot I-16163 Genoa Italy

Legged robots can outperform wheeled machines for most navigation tasks across unknown and rough terrains. For such tasks, visual feedback is a fundamental asset to provide robots with terrain awareness. However, robust dynamic locomotion on difficult terrains with real-time performance guarantees remains a challenge. We present here a real-time, dynamic foothold adaptation strategy based on visual feedback. Our method adjusts the landing position of the feet in a fully reactive manner, using only on-board computers and sensors. The correction is computed and executed continuously along the swing phase trajectory of each leg. To efficiently adapt the landing position, we implement a self-supervised foothold classifier based on a convolutional neural network. Our method results in an up to 200 times faster computation with respect to the full-blown heuristics. Our goal is to react to visual stimuli from the environment, bridging the gap between blind reactive locomotion and purely vision-based planning strategies. We assess the performance of our method on the dynamic quadruped robot HyQ, executing static and dynamic gaits (at speeds up to 0.5 m/s) in both simulated and real scenarios;the benefit of safe foothold adaptation is clearly demonstrated by the overall robot behavior.

关键词： Legged Robots Reactive and Sensor-Based Planning deep learning in robotics and automation

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Distributed Proprioception of 3D Configuration in Soft, Sensorized Robots via deep learning

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IEEE robotics AND automation LETTERS 2020年第2期5卷 3299-3306页

作者： Truby, Ryan L. Della Santina, Cosimo Rus, Daniela MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

Creating soft robots with sophisticated, autonomous capabilities requires these systems to possess reliable, on-line proprioception of 3D configuration through integrated soft sensors. We present a framework for predicting a soft robot's 3D configuration via deep learning using feedback from a soft, proprioceptive sensor skin. Our framework introduces a kirigami-enabled strategy for rapidly sensorizing soft robots using off-the-shelf materials, a general kinematic description for soft robot geometry, and an investigation of neural network designs for predicting soft robot configuration. Even with hysteretic, non-monotonic feedback from the piezoresistive sensors, recurrent neural networks show potential for predicting our new kinematic parameters and, thus, the robot's configuration. One trained neural network closely predicts steady-state configuration during operation, though complete dynamic behavior is not fully captured. We validate our methods on a trunk-like arm with 12 discrete actuators and 12 proprioceptive sensors. As an essential advance in soft robotic perception, we anticipate our framework will open new avenues towards closed loop control in soft robotics.

关键词： Modeling control and learning for soft robots soft sensors and actuators deep learning in robotics and automation

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learning Long-Range Perception Using Self-Supervision From Short-Range Sensors and Odometry

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IEEE robotics AND automation LETTERS 2019年第2期4卷 1279-1286页

作者： Nava, Mirko Guzzi, Jerome Chavez-Garcia, R. Omar Gambardella, Luca M. Giusti, Alessandro USI SUPSI Dalle Molle Inst Artificial Intelligence IDSIA CH-6928 Lugano Switzerland

We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor (such as a camera). We assume that the former is directly related to some piece of information to be perceived (such as the presence of an obstacle in a given position), whereas the latter is information rich but hard to interpret directly. We instantiate and implement the approach on a small mobile robot to detect obstacles at various distances using the video stream of the robot's forward-pointing camera, by training a convolutional neural network on automatically-acquired datasets. We quantitatively evaluate the quality of the predictions on unseen scenarios, qualitatively evaluate robustness to different operating conditions, and demonstrate usage as the sole input of an obstacle-avoidance controller. We additionally instantiate the approach on a different simulated scenario with complementary characteristics, to exemplify the generality of our contribution.

关键词： Range sensing computer vision for other robotic applications deep learning in robotics and automation

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Scene Compliant Trajectory Forecast With Agent-Centric Spatio-Temporal Grids

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2816-2823页

作者： Ridel, Daniela Deo, Nachiket Wolf, Denis Trivedi, Mohan Univ Sao Paulo Mobile Robot Lab BR-13566 Sao Carlos SP Brazil Univ Calif San Diego Lab Intelligent & Safe Automobiles San Diego CA 92092 USA

Forecasting long-term human motion is a challenging task due to the non-linearity, multi-modality and inherent uncertainty in future trajectories. The underlying scene and past motion of agents can provide useful cues to predict their future motion. However, the heterogeneity of the two inputs poses a challenge for learning a joint representation of the scene and past trajectories. To address this challenge, we propose a model based on grid representations to forecast agent trajectories. We represent the past trajectories of agents using binary 2-D grids, and the underlying scene as a RGB birds-eye view (BEV) image, with an agent-centric frame of reference. We encode the scene and past trajectories using convolutional layers and generate trajectory forecasts using a Convolutional LSTM (ConvLSTM) decoder. Results on the publicly available Stanford Drone Dataset (SDD) show that our model outperforms prior approaches and outputs realistic future trajectories that comply with scene structure and past motion.

关键词： Multimodal trajectory forecasting motion and path planning deep learning in robotics and automation

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Inference Over Distribution of Posterior Class Probabilities for Reliable Bayesian Classification and Object-Level Perception

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IEEE robotics AND automation LETTERS 2018年第4期3卷 4329-4336页

作者： Tchuiev, Vladimir Indelman, Vadim Technion Israel Inst Technol Dept Aerosp Engn IL-32000 Haifa Israel

State of the art Bayesian classification approaches typically maintain a posterior distribution over possible classes given available sensor observations (images). Yet, while these approaches fuse all classifier outputs thus far, they do not provide any indication regarding how reliable the posterior classification is, thus limiting its functionality in terms of autonomous systems and robotics. On the other hand, current deep learning based classifiers provide an uncertainty measure, thereby quantifying model uncertainty. However, they do so on a single frame basis and do not consider a sequential framework. In this letter, we develop a novel approach that infers a distribution over posterior class probabilities, while accounting for model uncertainty. This distribution enables reasoning about uncertainty in the posterior classification and, therefore, is of prime importance for robust classification, object-level perception in uncertain and ambiguous scenarios, and for safe autonomy in general. The distribution of the posterior class probability has no known analytical solution;thus, we propose to approximate this distribution via sampling. We evaluate our approach in simulation and using real images fed into a convolutional neural network classifier.

关键词： deep learning in robotics and automation recognition

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learning Fast Adaptation With Meta Strategy Optimization

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IEEE robotics AND automation LETTERS 2020年第2期5卷 2950-2957页

作者： Yu, Wenhao Tan, Jie Bai, Yunfei Coumans, Erwin Ha, Sehoon Google Robot Mountain View CA 94043 USA Georgia Inst Technol Atlanta GA 30332 USA

The ability to walk in new scenarios is a key milestone on the path toward real-world applications of legged robots. In this work, we introduce Meta Strategy Optimization, a meta-learning algorithm for training policies with latent variable inputs that can quickly adapt to new scenarios with a handful of trials in the target environment. The key idea behind MSO is to expose the same adaptation process, Strategy Optimization (SO), to both the training and testing phases. This allows MSO to effectively learn locomotion skills as well as a latent space that is suitable for fast adaptation. We evaluate our method on a real quadruped robot and demonstrate successful adaptation in various scenarios, including sim-to-real transfer, walking with a weakened motor, or climbing up a slope. Furthermore, we quantitatively analyze the generalization capability of the trained policy in simulated environments. Both real and simulated experiments show that our method outperforms previous methods in adaptation to novel tasks.

关键词： Optimization Training Legged locomotion Adaptation models Physics Task analysis deep learning in robotics and automation learning and adaptive systems legged robots

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deepTIO: A deep Thermal-Inertial Odometry With Visual Hallucination

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IEEE robotics AND automation LETTERS 2020年第2期5卷 1672-1679页

作者： Saputra, Muhamad Risqi U. de Gusmao, Pedro P. B. Lu, Chris Xiaoxuan Almalioglu, Yasin Rosa, Stefano Chen, Changhao Wahlstrom, Johan Wang, Wei Markham, Andrew Trigoni, Niki Univ Oxford Dept Comp Sci Oxford OX1 3QD England

Visual odometry shows excellent performance in a wide range of environments. However, in visually-denied scenarios (e.g. heavy smoke or darkness), pose estimates degrade or even fail. Thermal cameras are commonly used for perception and inspection when the environment has low visibility. However, their use in odometry estimation is hampered by the lack of robust visual features. In part, this is as a result of the sensor measuring the ambient temperature profile rather than scene appearance and geometry. To overcome this issue, we propose a deep Neural Network model for thermal-inertial odometry (deepTIO) by incorporating a visual hallucination network to provide the thermal network with complementary information. The hallucination network is taught to predict fake visual features from thermal images by using Huber loss. We also employ selective fusion to attentively fuse the features from three different modalities, i.e thermal, hallucination, and inertial features. Extensive experiments are performed in hand-held and mobile robot data in benign and smoke-filled environments, showing the efficacy of the proposed model.

关键词： Localization sensor fusion deep learning in robotics and automation thermal-inertial odometry

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